A multivariate empirical Bayes statistic for replicated microarray time course data
A multivariate empirical Bayes statistic for replicated microarray time course data
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DOI：
10.1214/009053606000000759
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发表时间：
2006-10-01
影响因子：
4.5
通讯作者：
Speed, Terence P.
中科院分区：
文献类型：
--
作者：
Tai, Yu Chuan;Speed, Terence P.
在本文中，我们推导出一个和两个样本的多元经验贝叶斯统计（MB统计）排序基因的顺序，从纵向重复的发育微阵列时间过程实验。我们首先使用共轭先验来开发我们的单样本多变量经验贝叶斯框架的零假设，预期的时间曲线保持在0。这导致我们的单样本MB统计量和单样本T-2统计量，单样本Hotelling T-2统计量的变体。MB统计量和T-2统计量均可用于按照非零均值证据的顺序对基因进行排名，并结合跨时间点、调节和复制的相关结构。我们还推导出相应的MB统计量和T-2统计量的一个样本的问题，其中的零假设状态，预期的时间轮廓是恒定的，并为两个样本的问题，其中的零假设是，两个预期的时间轮廓是相同的。
In this paper we derive one- and two-sample multivariate empirical Bayes statistics (the MB-statistics) to rank genes in order of interest from longitudinal replicated developmental microarray time course experiments. We first use conjugate priors to develop our one-sample multivariate empirical Bayes framework for the null hypothesis that the expected temporal profile stays at 0. This leads to our one-sample MB-statistic and a one-sample T-2-statistic, a variant of the one-sample Hotelling T-2-statistic. Both the MB-statistic and T-2-statistic can be used to rank genes in the order of evidence of nonzero mean, incorporating the correlation structure across time points, moderation and replication. We also derive the corresponding MB-statistics and T-2-statistics for the one-sample problem where the null hypothesis states that the expected temporal profile is constant, and for the two-sample problem where the null hypothesis is that two expected temporal profiles are the same.